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International Conference on Recent Trends in Image Processing and Pattern Recognition

RTIP2R 2018: Recent Trends in Image Processing and Pattern Recognition pp 395–406Cite as

Scale Invariant Texture Representation Using Galois Field for Image Classification

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1035))

Abstract

This paper presents a Galois Field method to scale invariant representation of texture image. The method is based on addition of neighbours in Galois Field. Scale invariance is achieved by considering the neighbours at different levels. The texture is represented using the features extracted by transforming it into a Galois Field based addition. Then the normalized cumulative histogram (NCH) bin values are considered as textures. For scale invariance, features are extracted at different levels. Thus obtained features are used for scale invariant classification. The average classification accuracy of 80.77%, 91.74%, 98.52% and 74.08% is achieved for Mondial Marmi, Brodatz, Vectorial and Outex datasets at level 3. The features can be used for suitable applications.

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Authors and Affiliations

  1. Karnatak University, Dharwad, 580003, India

    S. Shivashankar & Medha Kudari

Authors
  1. S. Shivashankar
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  2. Medha Kudari
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Correspondence to Medha Kudari .

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Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

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Shivashankar, S., Kudari, M. (2019). Scale Invariant Texture Representation Using Galois Field for Image Classification. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1035. Springer, Singapore. https://doi.org/10.1007/978-981-13-9181-1_35

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  • DOI: https://doi.org/10.1007/978-981-13-9181-1_35

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9180-4

  • Online ISBN: 978-981-13-9181-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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